Overview

Dataset statistics

Number of variables19
Number of observations846
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory125.7 KiB
Average record size in memory152.2 B

Variable types

NUM18
CAT1

Reproduction

Analysis started2020-08-25 02:01:08.214295
Analysis finished2020-08-25 02:02:01.372932
Duration53.16 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

SCATTER RATIO is highly correlated with DISTANCE CIRCULARITY and 4 other fieldsHigh correlation
DISTANCE CIRCULARITY is highly correlated with SCATTER RATIO and 1 other fieldsHigh correlation
ELONGATEDNESS is highly correlated with DISTANCE CIRCULARITY and 4 other fieldsHigh correlation
PR AXISRECTANGULAR is highly correlated with SCATTER RATIO and 3 other fieldsHigh correlation
LENGTHRECTANGULAR is highly correlated with CIRCULARITYHigh correlation
CIRCULARITY is highly correlated with LENGTHRECTANGULAR and 1 other fieldsHigh correlation
MAJORVARIANCE is highly correlated with SCATTER RATIO and 3 other fieldsHigh correlation
MINORVARIANCE is highly correlated with SCATTER RATIO and 3 other fieldsHigh correlation
GYRATIONRADIUS is highly correlated with CIRCULARITYHigh correlation
MINORSKEWNESS has 77 (9.1%) zeros Zeros
MINORKURTOSIS has 30 (3.5%) zeros Zeros

Variables

COMPACTNESS
Real number (ℝ≥0)

Distinct count44
Unique (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.67848699763593
Minimum73.0
Maximum119.0
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2020-08-25T02:02:01.420955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile82
Q187
median93
Q3100
95-th percentile108
Maximum119
Range46
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.234474253
Coefficient of variation (CV)0.08790144373
Kurtosis-0.5352753539
Mean93.678487
Median Absolute Deviation (MAD)6
Skewness0.3812706326
Sum79252
Variance67.80656623
2020-08-25T02:02:01.524838image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
89607.1%
 
86485.7%
 
93455.3%
 
85455.3%
 
90425.0%
 
91394.6%
 
94354.1%
 
88344.0%
 
96303.5%
 
97303.5%
 
98303.5%
 
87283.3%
 
95273.2%
 
104273.2%
 
100263.1%
 
92253.0%
 
101232.7%
 
84202.4%
 
82192.2%
 
107192.2%
 
106192.2%
 
103182.1%
 
99172.0%
 
102172.0%
 
83172.0%
 
Other values (19)10612.5%
 
ValueCountFrequency (%) 
7310.1%
 
7610.1%
 
7720.2%
 
7840.5%
 
7950.6%
 
80121.4%
 
81131.5%
 
82192.2%
 
83172.0%
 
84202.4%
 
ValueCountFrequency (%) 
11910.1%
 
11710.1%
 
11610.1%
 
11530.4%
 
11410.1%
 
11330.4%
 
11220.2%
 
11140.5%
 
11070.8%
 
109141.7%
 

CIRCULARITY
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count27
Unique (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.861702127659576
Minimum33.0
Maximum59.0
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2020-08-25T02:02:01.636729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile36
Q140
median44
Q349
95-th percentile55
Maximum59
Range26
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.169865606
Coefficient of variation (CV)0.1375307961
Kurtosis-0.9249887105
Mean44.86170213
Median Absolute Deviation (MAD)5
Skewness0.2627987787
Sum37953
Variance38.0672416
2020-08-25T02:02:01.738707image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
43607.1%
 
45586.9%
 
44505.9%
 
46485.7%
 
42485.7%
 
38475.6%
 
37425.0%
 
39425.0%
 
40425.0%
 
36414.8%
 
54394.6%
 
47364.3%
 
41354.1%
 
55333.9%
 
48313.7%
 
53313.7%
 
51293.4%
 
49283.3%
 
52283.3%
 
50161.9%
 
35161.9%
 
56151.8%
 
57141.7%
 
3491.1%
 
5850.6%
 
Other values (2)30.4%
 
ValueCountFrequency (%) 
3320.2%
 
3491.1%
 
35161.9%
 
36414.8%
 
37425.0%
 
38475.6%
 
39425.0%
 
40425.0%
 
41354.1%
 
42485.7%
 
ValueCountFrequency (%) 
5910.1%
 
5850.6%
 
57141.7%
 
56151.8%
 
55333.9%
 
54394.6%
 
53313.7%
 
52283.3%
 
51293.4%
 
50161.9%
 

DISTANCE CIRCULARITY
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count63
Unique (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.08865248226951
Minimum40.0
Maximum112.0
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2020-08-25T02:02:01.849814image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile58
Q170
median80
Q398
95-th percentile107
Maximum112
Range72
Interquartile range (IQR)28

Descriptive statistics

Standard deviation15.7715327
Coefficient of variation (CV)0.1921280497
Kurtosis-0.9784712963
Mean82.08865248
Median Absolute Deviation (MAD)12
Skewness0.1072205856
Sum69447
Variance248.7412439
2020-08-25T02:02:01.966225image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
66465.4%
 
70414.8%
 
68293.4%
 
103283.3%
 
101273.2%
 
85273.2%
 
105263.1%
 
77253.0%
 
98253.0%
 
72253.0%
 
100242.8%
 
75242.8%
 
76232.7%
 
78222.6%
 
104212.5%
 
73202.4%
 
71192.2%
 
96192.2%
 
83192.2%
 
88182.1%
 
108172.0%
 
80172.0%
 
69161.9%
 
74161.9%
 
84161.9%
 
Other values (38)25630.3%
 
ValueCountFrequency (%) 
4010.1%
 
4210.1%
 
4410.1%
 
4710.1%
 
4910.1%
 
5020.2%
 
5160.7%
 
5220.2%
 
5391.1%
 
5450.6%
 
ValueCountFrequency (%) 
11210.1%
 
11040.5%
 
109151.8%
 
108172.0%
 
107131.5%
 
106121.4%
 
105263.1%
 
104212.5%
 
103283.3%
 
10260.7%
 

RADIUS RATIO
Real number (ℝ≥0)

Distinct count134
Unique (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.94089834515367
Minimum104.0
Maximum333.0
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2020-08-25T02:02:02.086449image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile120
Q1141
median167
Q3195
95-th percentile222
Maximum333
Range229
Interquartile range (IQR)54

Descriptive statistics

Standard deviation33.47218301
Coefficient of variation (CV)0.1981295432
Kurtosis0.3018252546
Mean168.9408983
Median Absolute Deviation (MAD)27
Skewness0.3907064648
Sum142924
Variance1120.387035
2020-08-25T02:02:02.188602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
197182.1%
 
162151.8%
 
125131.5%
 
169131.5%
 
139121.4%
 
150121.4%
 
186121.4%
 
130121.4%
 
136121.4%
 
199121.4%
 
133111.3%
 
201111.3%
 
141111.3%
 
191111.3%
 
209111.3%
 
194111.3%
 
123101.2%
 
160101.2%
 
140101.2%
 
203101.2%
 
183101.2%
 
154101.2%
 
15891.1%
 
17291.1%
 
16491.1%
 
Other values (109)56266.4%
 
ValueCountFrequency (%) 
10410.1%
 
10510.1%
 
10910.1%
 
11030.4%
 
11140.5%
 
11210.1%
 
11340.5%
 
11440.5%
 
11540.5%
 
11670.8%
 
ValueCountFrequency (%) 
33310.1%
 
32210.1%
 
30610.1%
 
25210.1%
 
25010.1%
 
24620.2%
 
23810.1%
 
23510.1%
 
23420.2%
 
23210.1%
 

PR AXIS ASPECT RATIO
Real number (ℝ≥0)

Distinct count37
Unique (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.69385342789598
Minimum47.0
Maximum138.0
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2020-08-25T02:02:02.297460image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile53
Q157
median61
Q365
95-th percentile71
Maximum138
Range91
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.888251172
Coefficient of variation (CV)0.1278612169
Kurtosis29.83624029
Mean61.69385343
Median Absolute Deviation (MAD)4
Skewness3.821560116
Sum52193
Variance62.22450655
2020-08-25T02:02:02.388240image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
64698.2%
 
59647.6%
 
62586.9%
 
56576.7%
 
60465.4%
 
63455.3%
 
57445.2%
 
58435.1%
 
61425.0%
 
54384.5%
 
65384.5%
 
55374.4%
 
66374.4%
 
68344.0%
 
67293.4%
 
53273.2%
 
69263.1%
 
70182.1%
 
71151.8%
 
52141.7%
 
51111.3%
 
72101.2%
 
7491.1%
 
7370.8%
 
5050.6%
 
Other values (12)232.7%
 
ValueCountFrequency (%) 
4720.2%
 
4840.5%
 
4930.4%
 
5050.6%
 
51111.3%
 
52141.7%
 
53273.2%
 
54384.5%
 
55374.4%
 
56576.7%
 
ValueCountFrequency (%) 
13810.1%
 
13310.1%
 
12620.2%
 
10510.1%
 
10310.1%
 
10210.1%
 
9710.1%
 
7610.1%
 
7550.6%
 
7491.1%
 

MAX LENGTH ASPECT RATIO
Real number (ℝ≥0)

Distinct count21
Unique (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.567375886524824
Minimum2.0
Maximum55.0
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2020-08-25T02:02:02.489765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q17
median8
Q310
95-th percentile12
Maximum55
Range53
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.601216661
Coefficient of variation (CV)0.5370625407
Kurtosis58.37545547
Mean8.567375887
Median Absolute Deviation (MAD)2
Skewness6.778393619
Sum7248
Variance21.17119476
2020-08-25T02:02:02.585747image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
716819.9%
 
613215.6%
 
811313.4%
 
1011213.2%
 
1110812.8%
 
99411.1%
 
5516.0%
 
12303.5%
 
4182.1%
 
340.5%
 
1330.4%
 
2220.2%
 
5220.2%
 
4920.2%
 
5510.1%
 
4810.1%
 
4310.1%
 
1910.1%
 
210.1%
 
4610.1%
 
2510.1%
 
ValueCountFrequency (%) 
210.1%
 
340.5%
 
4182.1%
 
5516.0%
 
613215.6%
 
716819.9%
 
811313.4%
 
99411.1%
 
1011213.2%
 
1110812.8%
 
ValueCountFrequency (%) 
5510.1%
 
5220.2%
 
4920.2%
 
4810.1%
 
4610.1%
 
4310.1%
 
2510.1%
 
2220.2%
 
1910.1%
 
1330.4%
 

SCATTER RATIO
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count131
Unique (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.83924349881798
Minimum112.0
Maximum265.0
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2020-08-25T02:02:02.692459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum112
5-th percentile125
Q1146.25
median157
Q3198
95-th percentile222
Maximum265
Range153
Interquartile range (IQR)51.75

Descriptive statistics

Standard deviation33.24497804
Coefficient of variation (CV)0.196903145
Kurtosis-0.61586789
Mean168.8392435
Median Absolute Deviation (MAD)20
Skewness0.6057790031
Sum142838
Variance1105.228565
2020-08-25T02:02:02.797222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
150354.1%
 
149293.4%
 
151293.4%
 
157212.5%
 
152192.2%
 
148182.1%
 
153172.0%
 
155161.9%
 
147141.7%
 
154141.7%
 
161131.5%
 
156131.5%
 
146131.5%
 
142121.4%
 
133121.4%
 
220111.3%
 
140111.3%
 
135111.3%
 
159111.3%
 
218111.3%
 
160111.3%
 
219101.2%
 
222101.2%
 
214101.2%
 
208101.2%
 
Other values (106)46555.0%
 
ValueCountFrequency (%) 
11210.1%
 
11440.5%
 
11520.2%
 
11640.5%
 
11720.2%
 
11850.6%
 
11960.7%
 
12030.4%
 
12110.1%
 
12280.9%
 
ValueCountFrequency (%) 
26510.1%
 
26210.1%
 
26110.1%
 
26010.1%
 
25730.4%
 
25610.1%
 
25510.1%
 
25210.1%
 
25110.1%
 
25020.2%
 

ELONGATEDNESS
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count35
Unique (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.9338061465721
Minimum26.0
Maximum61.0
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2020-08-25T02:02:02.908749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile30
Q133
median43
Q346
95-th percentile54
Maximum61
Range35
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.81155972
Coefficient of variation (CV)0.1908339452
Kurtosis-0.8640680986
Mean40.93380615
Median Absolute Deviation (MAD)6
Skewness0.04784505627
Sum34630
Variance61.02046526
2020-08-25T02:02:03.211762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
31738.6%
 
45738.6%
 
44728.5%
 
43607.1%
 
46597.0%
 
30505.9%
 
32445.2%
 
33283.3%
 
42273.2%
 
35253.0%
 
50253.0%
 
41232.7%
 
48232.7%
 
34212.5%
 
52202.4%
 
36192.2%
 
40192.2%
 
39182.1%
 
51182.1%
 
37182.1%
 
38182.1%
 
49161.9%
 
47141.7%
 
57121.4%
 
54101.2%
 
Other values (10)617.2%
 
ValueCountFrequency (%) 
26101.2%
 
2770.8%
 
2870.8%
 
2920.2%
 
30505.9%
 
31738.6%
 
32445.2%
 
33283.3%
 
34212.5%
 
35253.0%
 
ValueCountFrequency (%) 
6110.1%
 
5940.5%
 
5840.5%
 
57121.4%
 
5660.7%
 
55101.2%
 
54101.2%
 
53101.2%
 
52202.4%
 
51182.1%
 

PR AXISRECTANGULAR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count13
Unique (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.58274231678487
Minimum17.0
Maximum29.0
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2020-08-25T02:02:03.331293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile17.25
Q119
median20
Q323
95-th percentile25
Maximum29
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.592138328
Coefficient of variation (CV)0.125937462
Kurtosis-0.393060044
Mean20.58274232
Median Absolute Deviation (MAD)2
Skewness0.7706844559
Sum17413
Variance6.71918111
2020-08-25T02:02:03.426494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1923928.3%
 
1812915.2%
 
2011613.7%
 
249010.6%
 
25586.9%
 
23526.1%
 
22485.7%
 
21475.6%
 
17435.1%
 
2691.1%
 
2891.1%
 
2750.6%
 
2910.1%
 
ValueCountFrequency (%) 
17435.1%
 
1812915.2%
 
1923928.3%
 
2011613.7%
 
21475.6%
 
22485.7%
 
23526.1%
 
249010.6%
 
25586.9%
 
2691.1%
 
ValueCountFrequency (%) 
2910.1%
 
2891.1%
 
2750.6%
 
2691.1%
 
25586.9%
 
249010.6%
 
23526.1%
 
22485.7%
 
21475.6%
 
2011613.7%
 

LENGTHRECTANGULAR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count66
Unique (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.99881796690306
Minimum118.0
Maximum188.0
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2020-08-25T02:02:03.535890image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum118
5-th percentile126.25
Q1137
median146
Q3159
95-th percentile173
Maximum188
Range70
Interquartile range (IQR)22

Descriptive statistics

Standard deviation14.51565157
Coefficient of variation (CV)0.09807951018
Kurtosis-0.7700982384
Mean147.998818
Median Absolute Deviation (MAD)11
Skewness0.2563591641
Sum125207
Variance210.7041406
2020-08-25T02:02:03.651128image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
144374.4%
 
145374.4%
 
143333.9%
 
147273.2%
 
146263.1%
 
134242.8%
 
148232.7%
 
131222.6%
 
141212.5%
 
162192.2%
 
158192.2%
 
142192.2%
 
139192.2%
 
156182.1%
 
128182.1%
 
163172.0%
 
127172.0%
 
149172.0%
 
135161.9%
 
161161.9%
 
132151.8%
 
150151.8%
 
138151.8%
 
133141.7%
 
137131.5%
 
Other values (41)32938.9%
 
ValueCountFrequency (%) 
11820.2%
 
11920.2%
 
12010.1%
 
12120.2%
 
12230.4%
 
12330.4%
 
12480.9%
 
125121.4%
 
126101.2%
 
127172.0%
 
ValueCountFrequency (%) 
18810.1%
 
18610.1%
 
18220.2%
 
18020.2%
 
17910.1%
 
17850.6%
 
17760.7%
 
17630.4%
 
17580.9%
 
17470.8%
 

MAJORVARIANCE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count128
Unique (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.62529550827423
Minimum130.0
Maximum320.0
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2020-08-25T02:02:03.769145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile144
Q1167
median178.5
Q3217
95-th percentile234
Maximum320
Range190
Interquartile range (IQR)50

Descriptive statistics

Standard deviation31.39483655
Coefficient of variation (CV)0.1664402246
Kurtosis0.1182733993
Mean188.6252955
Median Absolute Deviation (MAD)19.5
Skewness0.651813865
Sum159577
Variance985.6357617
2020-08-25T02:02:03.876839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
170344.0%
 
169293.4%
 
173253.0%
 
175202.4%
 
168182.1%
 
171172.0%
 
172172.0%
 
174172.0%
 
166161.9%
 
226161.9%
 
167151.8%
 
223141.7%
 
228141.7%
 
184141.7%
 
229131.5%
 
189131.5%
 
164131.5%
 
219131.5%
 
214131.5%
 
179131.5%
 
202121.4%
 
176121.4%
 
232111.3%
 
177111.3%
 
180111.3%
 
Other values (103)44552.6%
 
ValueCountFrequency (%) 
13010.1%
 
13110.1%
 
13210.1%
 
13410.1%
 
13560.7%
 
13620.2%
 
13760.7%
 
13830.4%
 
13940.5%
 
14040.5%
 
ValueCountFrequency (%) 
32010.1%
 
28810.1%
 
28710.1%
 
28530.4%
 
28030.4%
 
27810.1%
 
27520.2%
 
27220.2%
 
26920.2%
 
26720.2%
 

MINORVARIANCE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count424
Unique (%)50.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean439.9113475177305
Minimum184.0
Maximum1018.0
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2020-08-25T02:02:03.994649image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum184
5-th percentile229
Q1318.25
median364
Q3587
95-th percentile728
Maximum1018
Range834
Interquartile range (IQR)268.75

Descriptive statistics

Standard deviation176.6926136
Coefficient of variation (CV)0.4016550485
Kurtosis-0.2158537567
Mean439.9113475
Median Absolute Deviation (MAD)94.5
Skewness0.8358368316
Sum372165
Variance31220.27971
2020-08-25T02:02:04.089800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
32780.9%
 
33080.9%
 
33380.9%
 
32570.8%
 
33170.8%
 
36770.8%
 
33270.8%
 
35470.8%
 
32270.8%
 
34170.8%
 
32660.7%
 
35160.7%
 
37360.7%
 
32160.7%
 
31760.7%
 
32360.7%
 
33460.7%
 
34960.7%
 
34750.6%
 
33550.6%
 
36250.6%
 
34050.6%
 
31250.6%
 
37150.6%
 
25950.6%
 
Other values (399)69081.6%
 
ValueCountFrequency (%) 
18410.1%
 
19110.1%
 
19210.1%
 
19310.1%
 
19410.1%
 
19510.1%
 
19620.2%
 
19710.1%
 
20010.1%
 
20320.2%
 
ValueCountFrequency (%) 
101810.1%
 
99810.1%
 
98710.1%
 
98210.1%
 
96810.1%
 
96610.1%
 
95710.1%
 
95610.1%
 
95410.1%
 
92820.2%
 

GYRATIONRADIUS
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count143
Unique (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.70330969267138
Minimum109.0
Maximum268.0
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2020-08-25T02:02:04.199217image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum109
5-th percentile125
Q1149
median173
Q3198
95-th percentile228.75
Maximum268
Range159
Interquartile range (IQR)49

Descriptive statistics

Standard deviation32.54648984
Coefficient of variation (CV)0.1862957828
Kurtosis-0.4902489876
Mean174.7033097
Median Absolute Deviation (MAD)24.5
Skewness0.2802305409
Sum147799
Variance1059.274001
2020-08-25T02:02:04.313374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
186242.8%
 
171202.4%
 
176192.2%
 
172161.9%
 
174161.9%
 
214151.8%
 
173151.8%
 
185151.8%
 
218131.5%
 
139131.5%
 
177131.5%
 
127121.4%
 
157121.4%
 
145121.4%
 
178121.4%
 
158111.3%
 
144111.3%
 
162101.2%
 
137101.2%
 
159101.2%
 
184101.2%
 
151101.2%
 
216101.2%
 
20091.1%
 
18791.1%
 
Other values (118)51961.3%
 
ValueCountFrequency (%) 
10910.1%
 
11230.4%
 
11310.1%
 
11410.1%
 
11520.2%
 
11620.2%
 
11730.4%
 
11820.2%
 
11940.5%
 
12020.2%
 
ValueCountFrequency (%) 
26810.1%
 
26410.1%
 
26210.1%
 
26130.4%
 
26010.1%
 
25710.1%
 
25510.1%
 
25310.1%
 
25020.2%
 
24910.1%
 

MAJORSKEWNESS
Real number (ℝ≥0)

Distinct count39
Unique (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.46217494089835
Minimum59.0
Maximum135.0
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2020-08-25T02:02:04.434039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum59
5-th percentile63
Q167
median71.5
Q375
95-th percentile85
Maximum135
Range76
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.486974061
Coefficient of variation (CV)0.1033225137
Kurtosis11.37280051
Mean72.46217494
Median Absolute Deviation (MAD)4.5
Skewness2.072583136
Sum61303
Variance56.05478059
2020-08-25T02:02:04.536746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
72758.9%
 
71688.0%
 
74536.3%
 
67536.3%
 
70526.1%
 
73465.4%
 
69455.3%
 
68404.7%
 
66404.7%
 
75384.5%
 
64384.5%
 
65313.7%
 
76253.0%
 
63242.8%
 
85242.8%
 
77202.4%
 
81192.2%
 
62182.1%
 
82182.1%
 
80182.1%
 
78172.0%
 
83141.7%
 
86121.4%
 
61111.3%
 
79111.3%
 
Other values (14)364.3%
 
ValueCountFrequency (%) 
5910.1%
 
6020.2%
 
61111.3%
 
62182.1%
 
63242.8%
 
64384.5%
 
65313.7%
 
66404.7%
 
67536.3%
 
68404.7%
 
ValueCountFrequency (%) 
13510.1%
 
12710.1%
 
11910.1%
 
11810.1%
 
9910.1%
 
9710.1%
 
9110.1%
 
9020.2%
 
8910.1%
 
8850.6%
 

MINORSKEWNESS
Real number (ℝ≥0)

ZEROS

Distinct count23
Unique (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.377068557919622
Minimum0.0
Maximum22.0
Zeros77
Zeros (%)9.1%
Memory size6.7 KiB
2020-08-25T02:02:04.653234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q39
95-th percentile16
Maximum22
Range22
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.918352917
Coefficient of variation (CV)0.7712560831
Kurtosis0.08834803124
Mean6.377068558
Median Absolute Deviation (MAD)4
Skewness0.7737919046
Sum5395
Variance24.19019542
2020-08-25T02:02:04.746788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1819.6%
 
0779.1%
 
5718.4%
 
4708.3%
 
6677.9%
 
2637.4%
 
7617.2%
 
3576.7%
 
8475.6%
 
9465.4%
 
10364.3%
 
11323.8%
 
12303.5%
 
13263.1%
 
15192.2%
 
14182.1%
 
16121.4%
 
17111.3%
 
1860.7%
 
2150.6%
 
1940.5%
 
2240.5%
 
2030.4%
 
ValueCountFrequency (%) 
0779.1%
 
1819.6%
 
2637.4%
 
3576.7%
 
4708.3%
 
5718.4%
 
6677.9%
 
7617.2%
 
8475.6%
 
9465.4%
 
ValueCountFrequency (%) 
2240.5%
 
2150.6%
 
2030.4%
 
1940.5%
 
1860.7%
 
17111.3%
 
16121.4%
 
15192.2%
 
14182.1%
 
13263.1%
 

MINORKURTOSIS
Real number (ℝ≥0)

ZEROS

Distinct count41
Unique (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.599290780141844
Minimum0.0
Maximum41.0
Zeros30
Zeros (%)3.5%
Memory size6.7 KiB
2020-08-25T02:02:04.858568image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q319
95-th percentile29
Maximum41
Range41
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.931240269
Coefficient of variation (CV)0.7088684931
Kurtosis-0.1409573298
Mean12.59929078
Median Absolute Deviation (MAD)6
Skewness0.689325441
Sum10659
Variance79.76705275
2020-08-25T02:02:04.961944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
11465.4%
 
7445.2%
 
4414.8%
 
9404.7%
 
1384.5%
 
14384.5%
 
2374.4%
 
6374.4%
 
5364.3%
 
3323.8%
 
8323.8%
 
16313.7%
 
0303.5%
 
10303.5%
 
13303.5%
 
21293.4%
 
12283.3%
 
15232.7%
 
20222.6%
 
17202.4%
 
19202.4%
 
22202.4%
 
23192.2%
 
18172.0%
 
24131.5%
 
Other values (16)9311.0%
 
ValueCountFrequency (%) 
0303.5%
 
1384.5%
 
2374.4%
 
3323.8%
 
4414.8%
 
5364.3%
 
6374.4%
 
7445.2%
 
8323.8%
 
9404.7%
 
ValueCountFrequency (%) 
4110.1%
 
4010.1%
 
3910.1%
 
3860.7%
 
3630.4%
 
3540.5%
 
3410.1%
 
3350.6%
 
3260.7%
 
3150.6%
 

MAJORKURTOSIS
Real number (ℝ≥0)

Distinct count30
Unique (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.93262411347519
Minimum176.0
Maximum206.0
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2020-08-25T02:02:05.070880image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum176
5-th percentile179.25
Q1184
median188
Q3193
95-th percentile200
Maximum206
Range30
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.163949358
Coefficient of variation (CV)0.03262511907
Kurtosis-0.5940889244
Mean188.9326241
Median Absolute Deviation (MAD)4
Skewness0.2485407957
Sum159837
Variance37.99427169
2020-08-25T02:02:05.180493image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
188627.3%
 
189607.1%
 
187607.1%
 
186536.3%
 
192475.6%
 
190425.0%
 
191414.8%
 
183394.6%
 
180394.6%
 
181364.3%
 
193354.1%
 
185344.0%
 
195333.9%
 
184323.8%
 
179313.7%
 
194263.1%
 
182263.1%
 
196253.0%
 
197242.8%
 
198202.4%
 
199192.2%
 
201172.0%
 
200151.8%
 
20280.9%
 
20370.8%
 
Other values (5)151.8%
 
ValueCountFrequency (%) 
17630.4%
 
17750.6%
 
17840.5%
 
179313.7%
 
180394.6%
 
181364.3%
 
182263.1%
 
183394.6%
 
184323.8%
 
185344.0%
 
ValueCountFrequency (%) 
20610.1%
 
20420.2%
 
20370.8%
 
20280.9%
 
201172.0%
 
200151.8%
 
199192.2%
 
198202.4%
 
197242.8%
 
196253.0%
 

HOLLOWS RATIO
Real number (ℝ≥0)

Distinct count31
Unique (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.63238770685578
Minimum181.0
Maximum211.0
Zeros0
Zeros (%)0.0%
Memory size6.7 KiB
2020-08-25T02:02:05.298725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum181
5-th percentile183
Q1190.25
median197
Q3201
95-th percentile207
Maximum211
Range30
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation7.438797429
Coefficient of variation (CV)0.03802436558
Kurtosis-0.8134350379
Mean195.6323877
Median Absolute Deviation (MAD)5
Skewness-0.2263412803
Sum165505
Variance55.33570719
2020-08-25T02:02:05.407728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
198536.3%
 
197516.0%
 
199516.0%
 
196516.0%
 
201465.4%
 
195435.1%
 
183404.7%
 
200384.5%
 
184384.5%
 
202354.1%
 
194333.9%
 
193303.5%
 
203293.4%
 
191283.3%
 
204273.2%
 
185263.1%
 
182253.0%
 
205242.8%
 
206232.7%
 
192212.5%
 
187192.2%
 
189161.9%
 
186161.9%
 
190151.8%
 
188151.8%
 
Other values (6)536.3%
 
ValueCountFrequency (%) 
18120.2%
 
182253.0%
 
183404.7%
 
184384.5%
 
185263.1%
 
186161.9%
 
187192.2%
 
188151.8%
 
189161.9%
 
190151.8%
 
ValueCountFrequency (%) 
21140.5%
 
21080.9%
 
209121.4%
 
208151.8%
 
207121.4%
 
206232.7%
 
205242.8%
 
204273.2%
 
203293.4%
 
202354.1%
 

target
Categorical

Distinct count4
Unique (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
3
218
2
217
1
212
4
199
ValueCountFrequency (%) 
321825.8%
 
221725.7%
 
121225.1%
 
419923.5%
 
2020-08-25T02:02:05.559553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories (?)1
Unique unicode scripts (?)1
Unique unicode blocks (?)1
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
321825.8%
 
221725.7%
 
121225.1%
 
419923.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number846100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
321825.8%
 
221725.7%
 
121225.1%
 
419923.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common846100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
321825.8%
 
221725.7%
 
121225.1%
 
419923.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII846100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
321825.8%
 
221725.7%
 
121225.1%
 
419923.5%
 

Interactions

2020-08-25T02:01:09.368753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:09.513655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:09.657469image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:09.811656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:09.947921image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:10.091556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:10.233753image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:10.383681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:10.533195image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:10.677305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:10.830168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:10.980989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:11.119684image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:11.264863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:11.415044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:11.551595image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:11.689323image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:11.834282image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:11.977865image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:12.120536image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:12.264739image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:12.413500image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:12.561425image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:12.703834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:12.847508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:13.000547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:13.350593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:13.498764image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:13.656531image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:13.809795image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:13.954544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:14.111742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:14.265030image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:14.405421image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:14.550601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:14.706608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:14.861525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:15.018028image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:15.185355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:15.351465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:15.504731image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:15.660078image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:15.817086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:15.985711image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:16.163031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:16.320920image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:16.484604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:16.654614image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:16.805228image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:16.966918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:17.133651image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:17.283360image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:17.439052image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:17.604406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:17.763137image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T02:01:50.735103image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:50.884540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:51.031502image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:51.172582image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:51.321578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:51.468781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:51.610541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:51.746608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:51.888346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:52.037026image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:52.177461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:52.313682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:52.460808image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:52.595499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:52.734894image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:52.873174image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:53.017956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:53.165595image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:53.302097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:53.449878image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:53.601499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:53.927744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:54.081394image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:54.231742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:54.367378image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:54.500446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:54.643778image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:54.784142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:54.927928image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:55.078053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:55.236644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:55.384269image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:55.536866image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:55.691562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:55.850301image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:56.004653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:56.162648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:56.323857image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:56.488420image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:56.653166image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:56.890392image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:57.049407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:57.193805image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:57.340898image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:57.492904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:57.645663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:57.795528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:57.944730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:58.112517image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:58.259363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:58.407405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:58.557014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:58.718821image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:59.075979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:59.229449image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:59.384259image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:59.549683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:59.701678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:01:59.866062image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:00.023842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:00.169066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:00.316145image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:00.472660image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T02:02:05.705710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T02:02:06.058569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T02:02:06.398153image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T02:02:06.928375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T02:02:00.772837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T02:02:01.187513image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

COMPACTNESSCIRCULARITYDISTANCE CIRCULARITYRADIUS RATIOPR AXIS ASPECT RATIOMAX LENGTH ASPECT RATIOSCATTER RATIOELONGATEDNESSPR AXISRECTANGULARLENGTHRECTANGULARMAJORVARIANCEMINORVARIANCEGYRATIONRADIUSMAJORSKEWNESSMINORSKEWNESSMINORKURTOSISMAJORKURTOSISHOLLOWS RATIOtarget
095.043.096.0202.065.010.0189.035.022.0143.0217.0534.0166.071.06.027.0190.0197.01
196.052.0104.0222.067.09.0198.033.023.0163.0217.0589.0226.067.012.020.0192.0201.01
2107.052.0101.0218.064.011.0202.033.023.0164.0219.0610.0192.065.017.02.0197.0206.01
397.037.078.0181.062.08.0161.041.020.0131.0182.0389.0117.062.02.028.0203.0211.01
496.054.0104.0175.058.010.0215.031.024.0175.0221.0682.0222.075.013.023.0186.0194.01
597.055.0103.0197.063.011.0215.031.024.0172.0219.0677.0219.075.05.024.0185.0194.01
6105.054.0105.0213.067.010.0200.033.023.0163.0214.0597.0214.068.010.020.0190.0198.01
7105.052.0107.0207.060.011.0218.031.024.0167.0221.0701.0197.066.00.020.0191.0203.01
889.041.075.0143.056.07.0146.046.019.0137.0170.0317.0156.076.018.05.0184.0188.01
986.041.066.0129.055.07.0135.050.018.0136.0154.0266.0165.074.03.04.0180.0187.01

Last rows

COMPACTNESSCIRCULARITYDISTANCE CIRCULARITYRADIUS RATIOPR AXIS ASPECT RATIOMAX LENGTH ASPECT RATIOSCATTER RATIOELONGATEDNESSPR AXISRECTANGULARLENGTHRECTANGULARMAJORVARIANCEMINORVARIANCEGYRATIONRADIUSMAJORSKEWNESSMINORSKEWNESSMINORKURTOSISMAJORKURTOSISHOLLOWS RATIOtarget
83691.041.084.0141.057.09.0149.045.019.0143.0170.0330.0158.072.09.014.0189.0199.04
83790.048.085.0157.064.011.0161.043.020.0167.0175.0375.0186.074.03.016.0185.0195.04
83885.035.064.0129.057.06.0116.057.017.0125.0138.0200.0123.065.01.023.0196.0203.04
83989.047.084.0133.055.011.0157.044.020.0160.0169.0354.0176.074.05.09.0182.0192.04
84091.042.066.0142.058.09.0134.050.018.0142.0163.0268.0164.069.06.05.0191.0197.04
84188.040.055.0114.053.07.0132.053.018.0139.0142.0249.0158.087.00.07.0176.0183.04
84286.039.062.0129.059.06.0116.057.017.0135.0137.0203.0145.064.07.09.0199.0204.04
84394.047.085.0333.0138.049.0155.043.019.0155.0320.0354.0187.0135.012.09.0188.0196.04
84486.040.066.0139.059.07.0122.054.017.0139.0145.0225.0143.063.07.011.0202.0208.04
84592.038.060.0130.062.05.0114.058.017.0132.0135.0194.0137.072.014.05.0190.0194.04